Tuesday, March 4, 2014

Divvy Bikes - Data Challenge

Divvy,
a bike sharing service in Chicago, recently announced a data challenge where they published approximately 750k
rows worth of bike trip data in Chicago. They challenged people to build data
visualizations that would reveal and showcase patterns in usage of the bike
sharing service.

My
submission is what I have titled the ‘Divvy Station Cockpit’. It is designed to
provide an in-depth analysis of a selected station and can be used to help
Divvy evaluate operations at existing stations and better plan for new ones.

The dashboard features
the following capabilities:

Major KPIs:Average Daily Trips – How many trips
depart from this station?Relative Station Popularity – How
does this station rank compared to others based on average daily departures?Duration – When people take trips
from this station, how long does it take them to reach their destination?Duration Histogram – How are these
trips distributed? Is there a cluster around a specific time in case of a
common destination (e.g. Wrigleyville to the loop)Day of First Trip – How long has
this station been operating? Weekly Trips Trend – Is traffic
increasing or decreasing? Keep in mind, the data set starts in the summer and runs through December – brrrr!

Aggregated Daily
Supply/Demand Curves
Throughout the day, bikes are coming and going from each Divvy station. In
order to plan capacity and estimate traffic, this tool looks at the aggregated
demand over the course each day of the week and can quickly identify how this
station is commonly used. Is this a station where many commuters depart in the
morning (increasing demand for bikes) but then return to in the evening
(increasing supply)? Or is it a reverse commute when supply builds up in the
morning and then is depleted in the evening? Alternatively, is the station more
tourist-focused where demand is more consistent over the course day, but much higher
on the weekends than weekdays? This tool can help Divvy operations plan the optimal allocation of bikes.

Frequent Destination Heat
Map
Although nearby stations are unsurprisingly common destinations, occasionally
there are clear trends where a riders flow to a common location further away.
This heat map indicates which destinations are the most common for riders
departing the selected station.

Ridership by Day and
Member
For a quick look at the type of riders that are frequenting the station, we look at average daily ridership by member
type and day of the week to get a better sense if it is used by subscription members or guests and when. This view can be used in conjunction
with the Daily Demand Curve views to find stations that have a commuter usage
pattern, but a larger share of Guests users, to target for marketing resources suggesting
riders purchase a subscription membership.

I
had a lot of fun with this viz and thanks to Divvy for making this data set available!